mloss.org new softwarehttp://mloss.orgUpdates and additions to mloss.orgenWed, 16 Aug 2017 11:53:40 -0000iLANN SVD. An incremental noniterative learning method for one layer feedforwar 1.0http://mloss.org/revision/view/2104/<html><p>In machine learning literature, and especially in the literature referring to artificial neural networks, most methods are iterative and operate in batch mode. However, many of the standard algorithms are not suitable for efficiently managing the emerging large-scale data sets obtained from new real-world applications. Novel proposals to address these challenges are mainly iterative approaches based on incremental or distributed learning algorithms. However, the state-of-the-art is such that there are few learning methods based on non-iterative approaches, which have certain advantages over iterative models in dealing more efficiently with these new challenges. We have developed a non-iterative, incremental and hyperparameter-free learning method for one-layer feedforward neural networks without hidden layers. This method efficiently obtains the optimal parameters of the network, regardless of whether the data contains a greater number of samples than variables or vice versa. It does this by using a square loss function that measures errors before the output activation functions and scales them by the slope of these functions at each data point. The outcome is a system of linear equations that obtain the network's weights and that is further transformed using Singular Value Decomposition. Experimental results demonstrate that the proposed method appropriately solves a wide range of classification problems and is able to efficiently deal with large-scale tasks.
</p></html>Oscar Fontenla Romero, Beatriz Perez Sanchez, Bertha Guijarro BerdinasWed, 16 Aug 2017 11:53:40 -0000http://mloss.org/software/rss/comments/2104http://mloss.org/revision/view/2104/neural networkssingular value decompositionincremental learningnoniterative learningKeLP 2.2.1http://mloss.org/revision/view/2103/<html><p>Many applications in information and computer technology domains deal with structured data.
For example, in Natural Language Processing (NLP), sentences are typically represented as syntactic parse trees or in Biology, chemical compounds can be represented as undirected graphs.
In contrast, most Machine Learning (ML) methods and toolkits represent data as feature vectors, whose definition and computation is typically costly, especially in case of structured data. For example, the number of times a substructure appears in a structure can be an important feature. However, the number of substructures in a tree grows exponentially with the size of its nodes leading to an exponential number of structural features, which cannot thus be fully exploited in practice.
A solution to the above-mentioned problem is given by Kernel Methods applied with kernel machines, e.g., SVMs or online learning models.
The Kernel-based Learning Platform is a Java framework that aims to facilitate kernel-based learning, in particular on structural data. It contains the implementation of several kernel machines as well as kernel functions, enabling an easy and agile definition of new methods over generic data representations, e.g., vectorial data or discrete structures, such as trees and strings. The framework has been designed to decouple kernel functions and learning algorithms thanks to the definition of specific interfaces. Once a new kernel function is implemented, it can be immediately used in all available kernel-machines, which include different online and batch algorithms for <em>Classification</em>, <em>Regression</em> and <em>Clustering</em>.
The library is highly interoperable: data objects, kernel functions and algorithms are serializable in <em>XML</em> and <em>JSON</em>, enabling the agile definition of kernel-based learning systems. Additionally, such engineering choice allows for defining kernel and algorithm combinations by simply changing parameters in the <em>XML</em> and <em>JSON</em> files (without the need of writing new code).
</p>
<p>Some available <strong>kernels</strong>:
</p>
<ul>
<li><p><em>Tree Kernels</em>: SubTreeKernel, SubSetTreeKernel, PartialTreeKernel, SmoothedPartialTreeKernel, CompositionallySmoothedPartialTreeKernel
</p>
</li>
<li><p><em>Graph Kernels</em>: ShortestPathKernel. Weisfeiler-Lehman Subtree Kernel for Graphs
</p>
</li>
<li><p><em>SequenceKernel</em>
</p>
</li>
<li><p><em>PreferenceKernel</em> and other kernels defined over pairs
</p>
</li>
<li><p><em>Standard Kernels</em>: LinearKernel, PolynomialKernel, RBFKernel, NormalizationKernel, LinearKernelCombination, KernelMultiplication
</p>
</li>
</ul>
<p>Some available <strong>algorithms</strong>:
</p>
<ul>
<li><p><em>Batch Learning</em>: OneClassSVM, C-SVM, nu-SVM, LinearSVM, LinearSVMRegression, epsilon-regression, Dual Coordinate Descent
</p>
</li>
<li><p><em>Online Learning</em>: Perceptron, PassiveAggressive, BudgetedPassiveAggressive, Stoptron, RandomizedPerceptronOnBudget, SoftConfidenceWeightedClassification
</p>
</li>
<li><p><em>Clustering</em>: KernelizedKMean
</p>
</li>
</ul>
<p>NEWS: using KeLP our group won the <a href="http://alt.qcri.org/semeval2017/task3/">SemEval 2017 Task 3 challenge on Community Question Answering</a> and <a href="http://alt.qcri.org/semeval2016/task3/">SemEval 2016 Task 3 challenge on Community Question Answering</a>
</p></html>Simone Filice, Giuseppe Castellucci, Danilo Croce, Roberto Basili, Giovanni Da San Martino, Alessandro MoschittiMon, 07 Aug 2017 17:20:39 -0000http://mloss.org/software/rss/comments/2103http://mloss.org/revision/view/2103/svmclassificationclusteringregressionkernelsonline learningkernel methodsgraph kernelsstructured datalinear modelstree kernelsLANN SVD. A noniterative SVD based learning algorithm for one layer neural nets 1.0http://mloss.org/revision/view/2102/<html><p>A non-iterative learning method for one-layer (no hidden layer) neural networks, where the weights can be calculated in a closed-form manner, thereby avoiding low convergence rate and also hyperparameter tuning. The proposed learning method, LANN-SVD in short, presents a good computational efficiency for large-scale data analytic.
</p></html>Oscar Fontenla Romero, Beatriz Perez Sanchez, Bertha Guijarro BerdinasMon, 07 Aug 2017 13:52:19 -0000http://mloss.org/software/rss/comments/2102http://mloss.org/revision/view/2102/singular value decompositionlarge scale data analyticsnoniterative learningone layer neural networksA framework for benchmarking of feature selection algorithms and cost functions v1.3http://mloss.org/revision/view/2101/<html><p>featsel is a framework especially designed for benchmarking of feature selection algorithms and cost functions. Through a clean object-oriented structure and coded in C++, this framework allows the user to easily include new algorithms and/or cost functions and to carry out fast computational experiments.
</p>
<p>Moreover, featsel allows the user to take advantage of the fact that the search space of a feature selection procedure can be organized into a Boolean lattice; to this end, several methods are included in this framework especially for Boolean manipulation of feature subsets.
</p>
<p>featsel is also accompained by several scripts to assist the user in the inclusion and removal of algorithms and cost functions, to carry out benchmarking experiments and to create tables and graphs with the obtained results.
</p></html>Marcelo S. Reis, Gustavo Estrela, Carlos Eduardo Ferreira, Junior BarreraFri, 04 Aug 2017 00:19:28 -0000http://mloss.org/software/rss/comments/2101http://mloss.org/revision/view/2101/feature selectioncombinatorial optimizationbenchmarkingboolean latticer-cran-CoxBoost 1.4http://mloss.org/revision/view/1313/<html><p>Cox models by likelihood based boosting for a single survival endpoint or competing risks: This package provides routines for fitting Cox models by likelihood based boosting for a single endpoint or in presence of competing risks
</p></html>Harald BinderTue, 01 Aug 2017 00:00:04 -0000http://mloss.org/software/rss/comments/1313http://mloss.org/revision/view/1313/r-cranr-cran-e1071 1.6-8http://mloss.org/revision/view/2061/<html><p>Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien: Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, ...
</p></html>David Meyer [aut, cre], Evgenia Dimitriadou [aut, cph], Kurt Hornik [aut], Andreas Weingessel [aut], Friedrich Leisch [aut], Chih-Chung Chang [ctb, cph] (libsvm C++-code), Chih-Chen Lin [ctb, cph] (liTue, 01 Aug 2017 00:00:04 -0000http://mloss.org/software/rss/comments/2061http://mloss.org/revision/view/2061/r-cranr-cran-Boruta 5.2.0http://mloss.org/revision/view/2053/<html><p>Wrapper Algorithm for All Relevant Feature Selection: An all relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies.
</p></html>Miron Bartosz Kursa [aut, cre], Witold Remigiusz Rudnicki [aut]Tue, 01 Aug 2017 00:00:03 -0000http://mloss.org/software/rss/comments/2053http://mloss.org/revision/view/2053/r-cranr-cran-caret 6.0-76http://mloss.org/revision/view/2076/<html><p>Classification and Regression Training: Misc functions for training and plotting classification and regression models.
</p></html>Max Kun, Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, Brenton Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, Andrew Ziem, Luca ScrTue, 01 Aug 2017 00:00:03 -0000http://mloss.org/software/rss/comments/2076http://mloss.org/revision/view/2076/r-cranpSpectralClustering 1.2http://mloss.org/revision/view/2100/<html><p>A generalized version of spectral clustering using the graph p-Laplacian, as proposed in the paper
</p>
<p>Thomas Buehler and Matthias Hein,
Spectral Clustering based on the graph p-Laplacian,
Proceedings of the 26th
International Conference on Machine Learning (ICML),
pages 81-88, 2009.
</p></html>Thomas Buehler,Matthias HeinSun, 30 Jul 2017 20:07:52 -0000http://mloss.org/software/rss/comments/2100http://mloss.org/revision/view/2100/spectral clusteringclusteringgraph partitioningKeBABS 1.5.4http://mloss.org/revision/view/2099/<html><p>The package provides functionality for kernel-based analysis of DNA, RNA, and amino acid sequences via SVM-based methods. As core functionality, kebabs implements following sequence kernels: spectrum kernel, mismatch kernel, gappy pair kernel, and motif kernel. Apart from an efficient implementation of standard position-independent functionality, the kernels are extended in a novel way to take the position of patterns into account for the similarity measure. Because of the flexibility of the kernel formulation, other kernels like the weighted degree kernel or the shifted weighted degree kernel with constant weighting of positions are included as special cases. An annotation-specific variant of the kernels uses annotation information placed along the sequence together with the patterns in the sequence. The package allows for the generation of a kernel matrix or an explicit feature representation in dense or sparse format for all available kernels which can be used with methods implemented in other R packages. With focus on SVM-based methods, kebabs provides a framework which simplifies the usage of existing SVM implementations in kernlab, e1071, and LiblineaR. Binary and multi-class classification as well as regression tasks can be used in a unified way without having to deal with the different functions, parameters, and formats of the selected SVM. As support for choosing hyperparameters, the package provides cross validation - including grouped cross validation, grid search and model selection functions. For easier biological interpretation of the results, the package computes feature weights for all SVMs and prediction profiles which show the contribution of individual sequence positions to the prediction result and indicate the relevance of sequence sections for the learning result and the underlying biological functions.
</p></html>Johannes Palme, Ulrich BodenhoferFri, 28 Jul 2017 09:55:04 -0000http://mloss.org/software/rss/comments/2099http://mloss.org/revision/view/2099/bioinformaticssupport vector machinesequence analysisclassificationkernelskernel methodssupervised learning